Principle:Deepset ai Haystack Pipeline Orchestration
| Knowledge Sources | |
|---|---|
| Domains | Software_Architecture, Workflow_Orchestration |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
A directed acyclic graph execution engine that orchestrates component execution through typed input/output connections.
Description
Pipeline orchestration is the pattern of connecting discrete processing components into a directed graph where data flows from producers to consumers through typed sockets. Each component declares its input and output types, and the orchestrator validates connections at build time, resolves execution order at runtime, and manages data routing between components. This pattern decouples component implementation from execution strategy, enabling reuse, serialization, and debugging of complex multi-step workflows.
Usage
Use pipeline orchestration when building multi-step NLP or ML workflows such as RAG (Retrieval-Augmented Generation), document processing, or evaluation pipelines. It provides a declarative way to wire components together, validate data flow, and execute with built-in tracing and error handling. Prefer this over manual function chaining when you need reproducibility, serialization, or visual inspection of the processing graph.
Theoretical Basis
Pipeline orchestration follows the dataflow programming paradigm:
Component Model:
- Each component declares typed input sockets and output sockets
- Components are pure functions: given inputs, they produce outputs
- No implicit state sharing between components
Execution Model:
- The pipeline maintains a priority queue of runnable components
- A component is runnable when all required inputs are available
- Execution proceeds greedily, running the highest-priority component first
- Greedy variadic inputs allow components to accept partial input sets
Pseudo-code:
# Abstract pipeline execution (NOT real implementation)
pipeline = create_pipeline()
pipeline.add_component("step_a", ComponentA())
pipeline.add_component("step_b", ComponentB())
pipeline.connect("step_a.output", "step_b.input")
# Execution resolves dependencies automatically
results = pipeline.run({"step_a": {"data": input_data}})